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  2. Learning curve (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Learning_curve_(machine...

    In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1] Typically, the number of training epochs or training set size is plotted on ...

  3. The Pile (dataset) - Wikipedia

    en.wikipedia.org/wiki/The_Pile_(dataset)

    The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [1] [2] It is composed of 22 smaller datasets, including 14 new ones. [1]

  4. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  5. Neural scaling law - Wikipedia

    en.wikipedia.org/wiki/Neural_scaling_law

    The size of the training dataset is usually quantified by the number of data points within it. Larger training datasets are typically preferred, as they provide a richer and more diverse source of information from which the model can learn. This can lead to improved generalization performance when the model is applied to new, unseen data. [4]

  6. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    The ImageNet training set contained 1.2 million images. The model was trained for 90 epochs over a period of five to six days using two Nvidia GTX 580 GPUs (3GB each). [1] These GPUs have a theoretical performance of 1.581 TFLOPS in float32 and were priced at US$500 upon release. [3] Each forward pass of AlexNet required approximately 4 GFLOPs. [4]

  7. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...

  8. Early stopping - Wikipedia

    en.wikipedia.org/wiki/Early_stopping

    These methods are employed in the training of many iterative machine learning algorithms including neural networks. Prechelt gives the following summary of a naive implementation of holdout-based early stopping as follows: [9] Split the training data into a training set and a validation set, e.g. in a 2-to-1 proportion.

  9. Learning rate - Wikipedia

    en.wikipedia.org/wiki/Learning_rate

    A learning rate schedule changes the learning rate during learning and is most often changed between epochs/iterations. This is mainly done with two parameters: decay and momentum. There are many different learning rate schedules but the most common are time-based, step-based and exponential. [4]